1. Field of the Invention
The present invention relates to fast adaptive control over an array antenna, and more specifically, to fast adaptive control over an array antenna using a so-called genetic algorithm.
2. Description of the Background Art
An adaptive array antenna is an antenna including a plurality of antenna elements, which eliminates unwanted signals by applying appropriate weights to signals from the antenna elements and then combining the weighted signals. Outputs from the antenna elements are shifted in amplitude and phase and then combined to vary the antenna's directivity.
FIG. 22 is a block diagram showing the structure of a conventional adaptive array antenna. In FIG. 22, the adaptive array antenna includes a weighting part 4 for applying a predetermined weight to signals from the array antenna constructed of a plurality of antennas, a weighting control part 5 for controlling the weights in the weighting part 4, and a summer 6 for combining the weighted signals from the weighting control part 5.
Receive signals in the array antenna are inputted to the weighting part 4 and the weighting control part 5. The weighting control part 5 calculates the weights for varying the antenna's directivity so as to receive only a desired wave with highest sensitivity. The calculated weights are inputted to the weighting part 4.
The weighting part 4 applies the weight to each inputted signal. The weighted signals are combined by the summer 6 and then outputted.
In such adaptive array antenna, the algorithm used in the weighting control part 5 for calculating the weights so as to receive only a desired wave with highest sensitivity is an important factor. A typical algorithm includes LMS (Least Mean Squares) and RLS (Recursive Least Squares), both conventionally used, which are described below.
The LMS algorithm uses an instantaneous estimate of gradient based on an input (receive) vector and a sample value of an error signal. In LMS, the operation required for renewing a weight once is given by EQU w(n)=w(n-1)+.mu.u(n)e*(n) EQU e(n)=d(n)-w.sup.H (n-1)u(n) (1)
where w is the weight vector, u is the receive vector representing data sets for the antenna elements, d is the training signal, e is the error signal, * is complex conjugate, H is complex conjugate transpose, and n is the renewal number.
FIG. 23 is a block diagram showing the structure of the adaptive array antenna for realizing the operation of the above equations (1).
On the other hand, the RLS algorithm finds, unlike LMS, an inverse matrix of a correlation vector. In RLS, the operation required for renewing a weight once is given by ##EQU1## where k and P are the vectors.
FIG. 24 is a block diagram showing the structure of the adaptive array antenna for realizing the operation of the above equations (2).
In comparison, LMS requires less amount of operation but with lower accuracy, while RLS requires more amount of operation with higher accuracy. To compare the amounts of operation required for weight renewal processing, assume that the number of antenna elements is 8, and each amount of operation for addition and subtraction for 16 bits is 1. The amounts of operation for 16 bits are 16 for multiplication and 32 (16.times.2=32) for division. The amounts of operation for the complex number are: 2 for addition and subtraction each; 66 (16+16+1+16+16+1=66) for multiplication; and 132 (66.times.2=132) for division.
First, for LMS, in the above equations (1), the amounts of operation are 546 (2+(66+2).times.8=546) for e(n), 800 ((2+66+16+16)--8=800) for w(n), and 1346 (546+800=1346) in total.
Next, for RLS, in the above equations (2), the amounts of operation are: 5953 (8.times.8.times.(66+2)+8.times.(66+2)+1+8.times.132=5953) for k(n), 4352 (8.times.8.times.66+8.times.8.times.2=4352) for P(n), 546 (2+(66+2).times.8=546) for e(n), and 544 (8.times.(66+2)=544) for w(n), and 11395 (546+5953+4352+544=11395) in total.
Therefore, the amount of operation in LMS is less than 12% of that in RLS, allowing fast data communications.
For example, consider that an adaptive array antenna is used in a radio LAN using a frequency band of 2.4 GHz. In such radio LAN, a typical symbol rate is 10 MHz. Since the response rate required for weighting in the adaptive array antenna is approximately ten times the symbol rate, the adaptive array antenna is required to have the response rate of approximately 100 MHz. In view of performance of the available hardware, however, it is very difficult to achieve such fast response through RLS. Therefore, LMS has been widely used for adaptive array antennas.
LMS, however, uses an instantaneous estimate of gradient, resulting in low accuracy in solution because solution may be corrected in an erroneous direction due to noise, despite small amount of operation. Further, the convergence rate to solution is lower compared to RLS. Thus, when fast response is required as in the above described radio LAN, weights have to be calculated before convergence to solution, and as a result accuracy in solution becomes low.
FIG. 25 is a graph showing a convergence rate to solution in an adaptive array antenna using the LMS algorithm. In FIG. 25, a dotted line represents a desired wave, a one-dot-chain line represents the allowable noise level to the desired wave, and other three lines represent interference waves. Referring to FIG. 25, the levels of all interference waves are not more than the noise level to the desired wave when the number of iterations of operation is 75 or more, which means a convergence to solution is slow.